import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib as mpl
import seaborn as sns
mpl.rcParams[‘font.sans-serif‘] = [u‘SimHei‘]
mpl.rcParams[‘axes.unicode_minus‘] = False
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error

#制造一些数据
x_data = np.random.rand(200,5)*10
w = np.array([2,4,6,8,10])
y = np.dot(x_data,w) + np.random.rand(200)*20 + 20
x_train,x_test,y_train,y_test = train_test_split(x_data,y)

#先试试决策回归树吧，都没试过
from sklearn.tree import DecisionTreeRegressor
dtr = DecisionTreeRegressor()
dtr.fit(x_train,y_train)
y_hat = dtr.predict(x_test)
print(‘决策回归树的误差：‘,mean_squared_error(y_test,y_hat))

#画图
fig,ax = plt.subplots()
fig.set_size_inches(10,6)
ax.plot(np.arange(len(y_hat)),y_hat,color = ‘r‘)
ax.plot(np.arange(len(y_hat)),y_test,color = ‘g‘)

#调参一下试试
from sklearn.model_selection import GridSearchCV
model_dtr = GridSearchCV(dtr,param_grid=({‘max_depth‘:np.arange(1,50)}),cv=10)
model_dtr.fit(x_train,y_train)
y_hat = model_dtr.predict(x_test)
print(model_dtr.best_params_)
print(‘决策回归树的误差：‘,mean_squared_error(y_test,y_hat))

from sklearn.datasets import load_digits
from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import accuracy_score
digits = load_digits()
x_data = digits.data
y_data = digits.target
x_train,x_test,y_train,y_test = train_test_split(x_data,y_data)
dtc = DecisionTreeClassifier()
dtc.fit(x_train,y_train)
y_hat = dtc.predict(x_test)
print(‘正确率‘,accuracy_score(y_hat,y_test))


#调个参数看看
model_dtc = GridSearchCV(dtc,param_grid=({‘max_depth‘:np.arange(1,20)}),cv=10)
model_dtc.fit(x_train,y_train)
print(‘最佳参数‘,model_dtc.best_params_)
y_hat = model_dtc.predict(x_test)
y_hat = dtc.predict(x_test)
print(‘正确率‘,accuracy_score(y_hat,y_test))